






import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
A_						:						Union[str, Any]						      =					pytest.mark.integration


A_						:						Any						      =					{'comet'}
A_						:						Tuple						      =					importlib.util.find_spec('fairseq') is not None

A_						:						List[Any]						      =					{'code_eval'}
A_						:						Union[str, Any]						      =					os.name == 'nt'

A_						:						Union[str, Any]						      =					{'bertscore', 'frugalscore', 'perplexity'}
A_						:						List[str]						      =					importlib.util.find_spec('transformers') is not None

def     snake_case   (UpperCAmelCase__    )			->							Tuple:
  @wraps(UpperCAmelCase__    )
  def wrapper(self      ,       UpperCAmelCase__    ):
    if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
      self.skipTest('"test requires Fairseq"'    )
    else:
      test_case(self      ,       UpperCAmelCase__    )

  return wrapper

def     snake_case   (UpperCAmelCase__    )			->							Any:
  @wraps(UpperCAmelCase__    )
  def wrapper(self      ,       UpperCAmelCase__    ):
    if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
      self.skipTest('"test requires transformers"'    )
    else:
      test_case(self      ,       UpperCAmelCase__    )

  return wrapper

def     snake_case   (UpperCAmelCase__    )			->							Optional[int]:
  @wraps(UpperCAmelCase__    )
  def wrapper(self      ,       UpperCAmelCase__    ):
    if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
      self.skipTest('"test not supported on Windows"'    )
    else:
      test_case(self      ,       UpperCAmelCase__    )

  return wrapper

def     snake_case   ()			->							List[Any]:
  UpperCamelCase_:   Optional[int]        =							[metric_dir.split(os.sep    )[-2] for metric_dir in glob.glob('./metrics/*/'    )]
  return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished




@parameterized.named_parameters(get_local_metric_names()   )
@for_all_test_methods(
    UpperCAmelCase_				,      UpperCAmelCase_				,      UpperCAmelCase_   )
@local
class 							_lowerCAmelCase(			parameterized.TestCase   ):





       """simple docstring"""







       a							:						Tuple										={}
       a							:						str										=None





       @pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'				)
       @pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning'				)
       def      _a       (   self    ,     _lowerCamelCase				):
         UpperCamelCase_:   Any        =							'[...]'
         UpperCamelCase_:   int        =							importlib.import_module(
             datasets.load.metric_module_factory(os.path.join('metrics'    ,     _lowerCamelCase				)				).module_path				)
         UpperCamelCase_:   List[Any]        =							datasets.load.import_main_class(metric_module.__name__    ,     dataset=_lowerCamelCase				)
         # check parameters
         UpperCamelCase_:   Optional[Any]        =							inspect.signature(metric._compute				).parameters
         self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()				)				)  # no **kwargs
         # run doctest
         with self.patch_intensive_calls(_lowerCamelCase    ,     metric_module.__name__				):
           with self.use_local_metrics():
             try:
               UpperCamelCase_:   Union[str, Any]        =							doctest.testmod(_lowerCamelCase    ,     verbose=_lowerCamelCase    ,     raise_on_error=_lowerCamelCase				)
             except doctest.UnexpectedException as e:
               raise e.exc_info[1]  # raise the exception that doctest caught
         self.assertEqual(results.failed    ,     0				)
         self.assertGreater(results.attempted    ,     1				)





       @slow
       def      _a       (   self    ,     _lowerCamelCase				):
         UpperCamelCase_:   Optional[Any]        =							'[...]'
         UpperCamelCase_:   Optional[int]        =							importlib.import_module(
             datasets.load.metric_module_factory(os.path.join('metrics'    ,     _lowerCamelCase				)				).module_path				)
         # run doctest
         with self.use_local_metrics():
           UpperCamelCase_:   Union[str, Any]        =							doctest.testmod(_lowerCamelCase    ,     verbose=_lowerCamelCase    ,     raise_on_error=_lowerCamelCase				)
         self.assertEqual(results.failed    ,     0				)
         self.assertGreater(results.attempted    ,     1				)





       @contextmanager
       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         if metric_name in self.INTENSIVE_CALLS_PATCHER:
           with self.INTENSIVE_CALLS_PATCHER[metric_name](_lowerCamelCase				):
             yield
         else:
           yield





       @contextmanager
       def      _a       (   self				):
         def load_local_metric(_lowerCamelCase    ,     *_lowerCamelCase    ,     **_lowerCamelCase				):
           return load_metric(os.path.join('metrics'    ,     _lowerCamelCase				)    ,     *_lowerCamelCase    ,     **_lowerCamelCase				)

         with patch('datasets.load_metric'				) as mock_load_metric:
           UpperCamelCase_:   List[Any]        =							load_local_metric
           yield





       @classmethod
       def      _a       (   cls    ,     _lowerCamelCase				):
         def wrapper(_lowerCamelCase				):
           UpperCamelCase_:   Optional[Any]        =							contextmanager(_lowerCamelCase				)
           UpperCamelCase_:   int        =							patcher
           return patcher

         return wrapper







@LocalMetricTest.register_intensive_calls_patcher('bleurt'    )
def     snake_case   (UpperCAmelCase__    )			->							Dict:
  import tensorflow.compat.va as tf
  from bleurt.score import Predictor

  tf.flags.DEFINE_string('sv'      ,       ''      ,       ''    )  # handle pytest cli flags




  class 							_lowerCAmelCase(			UpperCAmelCase_   ):





         """simple docstring"""







         def      _a       (   self    ,     _lowerCamelCase				):
           assert len(input_dict['input_ids']				) == 2
           return np.array([1.0_3, 1.0_4]				)

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
  with patch('bleurt.score._create_predictor'    ) as mock_create_predictor:
    UpperCamelCase_:   Dict        =							MockedPredictor()
    yield

@LocalMetricTest.register_intensive_calls_patcher('bertscore'    )
def     snake_case   (UpperCAmelCase__    )			->							Optional[Any]:
  import torch

  def bert_cos_score_idf(UpperCAmelCase__      ,       UpperCAmelCase__      ,       *UpperCAmelCase__      ,       **UpperCAmelCase__    ):
    return torch.tensor([[1.0, 1.0, 1.0]] * len(UpperCAmelCase__    )    )

  # mock get_model which is supposed to do download a bert model
  # mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
  with patch('bert_score.scorer.get_model'    ), patch(
      'bert_score.scorer.bert_cos_score_idf'    ) as mock_bert_cos_score_idf:
    UpperCamelCase_:   Dict        =							bert_cos_score_idf
    yield

@LocalMetricTest.register_intensive_calls_patcher('comet'    )
def     snake_case   (UpperCAmelCase__    )			->							List[str]:
  def load_from_checkpoint(UpperCAmelCase__    ):




    class 							_lowerCAmelCase:





           """simple docstring"""







           def      _a       (   self    ,     _lowerCamelCase    ,     *_lowerCamelCase    ,     **_lowerCamelCase				):
             assert len(_lowerCamelCase				) == 2
             UpperCamelCase_:   List[Any]        =							[0.1_9, 0.9_2]
             return scores, sum(_lowerCamelCase				) / len(_lowerCamelCase				)







    return Model()

  # mock load_from_checkpoint which is supposed to do download a bert model
  # mock load_from_checkpoint which is supposed to do download a bert model
  with patch('comet.download_model'    ) as mock_download_model:
    UpperCamelCase_:   Union[str, Any]        =							None
    with patch('comet.load_from_checkpoint'    ) as mock_load_from_checkpoint:
      UpperCamelCase_:   int        =							load_from_checkpoint
      yield

def     snake_case   ()			->							str:
  UpperCamelCase_:   Union[str, Any]        =							load_metric(os.path.join('metrics'      ,       'seqeval'    )    )
  UpperCamelCase_:   Any        =							'ERROR'
  UpperCamelCase_:   List[Any]        =							F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
  with pytest.raises(UpperCAmelCase__      ,       match=re.escape(UpperCAmelCase__    )    ):
    metric.compute(predictions=[]      ,       references=[]      ,       scheme=UpperCAmelCase__    )


import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel



class        A__:
						"""simple docstring"""






						def __init__(    self  ,      _lowercase  ,      _lowercase=13  ,      _lowercase=7  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=False  ,      _lowercase=True  ,      _lowercase=99  ,      _lowercase=32  ,      _lowercase=5  ,      _lowercase=4  ,      _lowercase=37  ,      _lowercase="gelu"  ,      _lowercase=0.1  ,      _lowercase=0.1  ,      _lowercase=512  ,      _lowercase=16  ,      _lowercase=2  ,      _lowercase=0.0_2  ,      _lowercase=3  ,      _lowercase=4  ,      _lowercase=None  ,      )     -> Tuple:
									a_						:						Union[str, Any] 							= parent
									a_						:						int 							= batch_size
									a_						:						int 							= seq_length
									a_						:						int 							= is_training
									a_						:						List[Any] 							= use_input_mask
									a_						:						str 							= use_token_type_ids
									a_						:						List[str] 							= use_labels
									a_						:						List[Any] 							= vocab_size
									a_						:						Dict 							= hidden_size
									a_						:						List[Any] 							= num_hidden_layers
									a_						:						str 							= num_attention_heads
									a_						:						str 							= intermediate_size
									a_						:						Optional[int] 							= hidden_act
									a_						:						Optional[int] 							= hidden_dropout_prob
									a_						:						int 							= attention_probs_dropout_prob
									a_						:						List[str] 							= max_position_embeddings
									a_						:						Optional[Any] 							= type_vocab_size
									a_						:						Any 							= type_sequence_label_size
									a_						:						List[Any] 							= initializer_range
									a_						:						List[str] 							= num_labels
									a_						:						Union[str, Any] 							= num_choices
									a_						:						Dict 							= scope





						def 			UpperCamelCase__					(    self    )     -> Any:
									a_						:						Dict 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size    )

									a_						:						Union[str, Any] 							= None
									if self.use_input_mask:
												a_						:						Optional[int] 							= random_attention_mask([self.batch_size, self.seq_length]    )

									a_						:						List[Any] 							= None
									if self.use_token_type_ids:
												a_						:						Any 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.type_vocab_size    )

									a_						:						Union[str, Any] 							= None
									a_						:						Dict 							= None
									a_						:						Any 							= None
									if self.use_labels:
												a_						:						Tuple 							= ids_tensor([self.batch_size]  ,      self.type_sequence_label_size    )
												a_						:						Optional[int] 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.num_labels    )
												a_						:						int 							= ids_tensor([self.batch_size]  ,      self.num_choices    )

									a_						:						Optional[int] 							= self.get_config()

									return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





						def 			UpperCamelCase__					(    self    )     -> Any:
									return OpenLlamaConfig(
									    vocab_size=self.vocab_size  ,      hidden_size=self.hidden_size  ,      num_hidden_layers=self.num_hidden_layers  ,      num_attention_heads=self.num_attention_heads  ,      intermediate_size=self.intermediate_size  ,      hidden_act=self.hidden_act  ,      hidden_dropout_prob=self.hidden_dropout_prob  ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,      max_position_embeddings=self.max_position_embeddings  ,      type_vocab_size=self.type_vocab_size  ,      is_decoder=_lowercase  ,      initializer_range=self.initializer_range  ,      use_stable_embedding=_lowercase  ,      )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase    )     -> Dict:
									a_						:						Optional[Any] 							= OpenLlamaModel(config=_lowercase    )
									model.to(_lowercase    )
									model.eval()
									a_						:						List[Any] 							= model(_lowercase  ,      attention_mask=_lowercase    )
									a_						:						Optional[Any] 							= model(_lowercase    )
									self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.hidden_size)    )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      )     -> Union[str, Any]:
									a_						:						int 							= True
									a_						:						Union[str, Any] 							= OpenLlamaModel(_lowercase    )
									model.to(_lowercase    )
									model.eval()
									a_						:						Optional[int] 							= model(
									    _lowercase  ,      attention_mask=_lowercase  ,      encoder_hidden_states=_lowercase  ,      encoder_attention_mask=_lowercase  ,      )
									a_						:						Any 							= model(
									    _lowercase  ,      attention_mask=_lowercase  ,      encoder_hidden_states=_lowercase  ,      )
									a_						:						Optional[int] 							= model(_lowercase  ,      attention_mask=_lowercase    )
									self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.hidden_size)    )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      )     -> Any:
									a_						:						Dict 							= OpenLlamaForCausalLM(config=_lowercase    )
									model.to(_lowercase    )
									model.eval()
									a_						:						int 							= model(_lowercase  ,      attention_mask=_lowercase  ,      labels=_lowercase    )
									self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.seq_length, self.vocab_size)    )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      )     -> List[str]:
									a_						:						Dict 							= True
									a_						:						Optional[int] 							= True
									a_						:						Dict 							= OpenLlamaForCausalLM(config=_lowercase    )
									model.to(_lowercase    )
									model.eval()

									# first forward pass
									a_						:						List[str] 							= model(
									    _lowercase  ,      attention_mask=_lowercase  ,      encoder_hidden_states=_lowercase  ,      encoder_attention_mask=_lowercase  ,      use_cache=_lowercase  ,      )
									a_						:						Optional[Any] 							= outputs.past_key_values

									# create hypothetical multiple next token and extent to next_input_ids
									a_						:						Optional[Any] 							= ids_tensor((self.batch_size, 3)  ,      config.vocab_size    )
									a_						:						Union[str, Any] 							= ids_tensor((self.batch_size, 3)  ,      vocab_size=2    )

									# append to next input_ids and
									a_						:						Any 							= torch.cat([input_ids, next_tokens]  ,      dim=-1    )
									a_						:						Any 							= torch.cat([input_mask, next_mask]  ,      dim=-1    )

									a_						:						Optional[int] 							= model(
									    _lowercase  ,      attention_mask=_lowercase  ,      encoder_hidden_states=_lowercase  ,      encoder_attention_mask=_lowercase  ,      output_hidden_states=_lowercase  ,      )["""hidden_states"""][0]
									a_						:						Dict 							= model(
									    _lowercase  ,      attention_mask=_lowercase  ,      encoder_hidden_states=_lowercase  ,      encoder_attention_mask=_lowercase  ,      past_key_values=_lowercase  ,      output_hidden_states=_lowercase  ,      )["""hidden_states"""][0]

									# select random slice
									a_						:						Optional[int] 							= ids_tensor((1,)  ,      output_from_past.shape[-1]    ).item()
									a_						:						Dict 							= output_from_no_past[:, -3:, random_slice_idx].detach()
									a_						:						Optional[int] 							= output_from_past[:, :, random_slice_idx].detach()

									self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

									# test that outputs are equal for slice
									self.parent.assertTrue(torch.allclose(_lowercase  ,      _lowercase  ,      atol=1e-3    )    )





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_						:						Any 							= self.prepare_config_and_inputs()
									(
									    (
									    a_
									)							,					(
									    a_
									)							,					(
									    a_
									)							,					(
									    a_
									)							,					(
									    a_
									)							,					(
									    a_
									)							,					(
									    a_
									)							,					
									)						:						Union[str, Any] 							= config_and_inputs
									a_						:						Tuple 							= {"""input_ids""": input_ids, """attention_mask""": input_mask}
									return config, inputs_dict







@require_torch
class        A__(a_,  a_,  a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				Tuple       					=			(
						    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
						)
						_A    :				int       					=			(OpenLlamaForCausalLM,) if is_torch_available() else ()
						_A    :				Optional[int]       					=			(
						    {
						        '''feature-extraction''': OpenLlamaModel,
						        '''text-classification''': OpenLlamaForSequenceClassification,
						        '''text-generation''': OpenLlamaForCausalLM,
						        '''zero-shot''': OpenLlamaForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_A    :				Tuple       					=			False
						_A    :				Any       					=			False





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Optional[int] 							= OpenLlamaModelTester(self    )
									a_						:						Optional[int] 							= ConfigTester(self  ,      config_class=_lowercase  ,      hidden_size=37    )





						def 			UpperCamelCase__					(    self    )     -> Any:
									self.config_tester.run_common_tests()





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_						:						Union[str, Any] 							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									a_						:						Any 							= self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
												a_						:						Dict 							= type
												self.model_tester.create_and_check_model(*_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									a_							,					a_						:						Tuple 							= self.model_tester.prepare_config_and_inputs_for_common()
									a_						:						Any 							= 3
									a_						:						List[str] 							= input_dict["""input_ids"""]
									a_						:						List[str] 							= input_ids.ne(1    ).to(_lowercase    )
									a_						:						int 							= ids_tensor([self.model_tester.batch_size]  ,      self.model_tester.type_sequence_label_size    )
									a_						:						int 							= OpenLlamaForSequenceClassification(_lowercase    )
									model.to(_lowercase    )
									model.eval()
									a_						:						Tuple 							= model(_lowercase  ,      attention_mask=_lowercase  ,      labels=_lowercase    )
									self.assertEqual(result.logits.shape  ,      (self.model_tester.batch_size, self.model_tester.num_labels)    )





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_							,					a_						:						Any 							= self.model_tester.prepare_config_and_inputs_for_common()
									a_						:						Union[str, Any] 							= 3
									a_						:						List[Any] 							= """single_label_classification"""
									a_						:						Dict 							= input_dict["""input_ids"""]
									a_						:						int 							= input_ids.ne(1    ).to(_lowercase    )
									a_						:						List[str] 							= ids_tensor([self.model_tester.batch_size]  ,      self.model_tester.type_sequence_label_size    )
									a_						:						Any 							= OpenLlamaForSequenceClassification(_lowercase    )
									model.to(_lowercase    )
									model.eval()
									a_						:						Any 							= model(_lowercase  ,      attention_mask=_lowercase  ,      labels=_lowercase    )
									self.assertEqual(result.logits.shape  ,      (self.model_tester.batch_size, self.model_tester.num_labels)    )





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									a_							,					a_						:						Union[str, Any] 							= self.model_tester.prepare_config_and_inputs_for_common()
									a_						:						Any 							= 3
									a_						:						List[str] 							= """multi_label_classification"""
									a_						:						Dict 							= input_dict["""input_ids"""]
									a_						:						Any 							= input_ids.ne(1    ).to(_lowercase    )
									a_						:						List[Any] 							= ids_tensor(
									    [self.model_tester.batch_size, config.num_labels]  ,      self.model_tester.type_sequence_label_size    ).to(torch.float    )
									a_						:						Optional[int] 							= OpenLlamaForSequenceClassification(_lowercase    )
									model.to(_lowercase    )
									model.eval()
									a_						:						int 							= model(_lowercase  ,      attention_mask=_lowercase  ,      labels=_lowercase    )
									self.assertEqual(result.logits.shape  ,      (self.model_tester.batch_size, self.model_tester.num_labels)    )





						@unittest.skip("""Open-Llama buffers include complex numbers, which breaks this test"""    )
						def 			UpperCamelCase__					(    self    )     -> Tuple:
									pass





						@parameterized.expand([("""linear""",), ("""dynamic""",)]    )
						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> str:
									a_							,					a_						:						Optional[int] 							= self.model_tester.prepare_config_and_inputs_for_common()
									a_						:						List[Any] 							= ids_tensor([1, 10]  ,      config.vocab_size    )
									a_						:						str 							= ids_tensor([1, int(config.max_position_embeddings * 1.5    )]  ,      config.vocab_size    )

									set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
									a_						:						Union[str, Any] 							= OpenLlamaModel(_lowercase    )
									original_model.to(_lowercase    )
									original_model.eval()
									a_						:						Union[str, Any] 							= original_model(_lowercase    ).last_hidden_state
									a_						:						str 							= original_model(_lowercase    ).last_hidden_state

									set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
									a_						:						int 							= {"""type""": scaling_type, """factor""": 1_0.0}
									a_						:						Union[str, Any] 							= OpenLlamaModel(_lowercase    )
									scaled_model.to(_lowercase    )
									scaled_model.eval()
									a_						:						Optional[int] 							= scaled_model(_lowercase    ).last_hidden_state
									a_						:						Tuple 							= scaled_model(_lowercase    ).last_hidden_state

									# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
									# maximum sequence length, so the outputs for the short input should match.
									if scaling_type == "dynamic":
												self.assertTrue(torch.allclose(_lowercase  ,      _lowercase  ,      atol=1e-5    )    )
									else:
												self.assertFalse(torch.allclose(_lowercase  ,      _lowercase  ,      atol=1e-5    )    )

									# The output should be different for long inputs
									self.assertFalse(torch.allclose(_lowercase  ,      _lowercase  ,      atol=1e-5    )    )


